The Modulation Transfer Function (MTF) is an important image quality metric typically used in the automotive domain. However, despite the fact that optical quality has an impact on the performance of computer vision i...
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Alzheimer’s disease (AD) is a type of neurological disorder that progresses over a period and affects a significant number of people worldwide. Early detection of AD is necessary for rapid intervention and improved o...
Alzheimer’s disease (AD) is a type of neurological disorder that progresses over a period and affects a significant number of people worldwide. Early detection of AD is necessary for rapid intervention and improved outcomes for patients. In this paper, we compare the performance of four widely used Deep Convolutional Neural Network (DCNN) architectures: CNN, VGG16, InceptionV3, and DenseNet121. The study focuses on the use of DCNNs for AD identification. Our dataset is collected from the Kaggle website and preprocessed such that the inputs are identical for every model. Several criteria are used to assess the DCNN architecture’s performance, including recall, accuracy, precision, and f1-score. By looking at individual architecture’s predictive capabilities, we may discover further the benefits and drawbacks of AD detection jobs. Also, transfer learning techniques employ pre-trained weights from large neuroimaging datasets to enhance model performance and generalization abilities. This study contributes to the increasing body of knowledge in AD research by identifying the ideal DCNN architecture for fast and accurate AD diagnosis.
Public transport services are essential for any citizen but critical for those with visual disorders. Many projects have been created to help blind people overpass public transport services' inherent barriers and ...
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With the rise of social media, particularly Twitter, textual data containing users’ opinions on various topics has significantly increased. So sentiment analysis, which involves detecting the polarity of emotions in ...
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ISBN:
(数字)9798331508913
ISBN:
(纸本)9798331508920
With the rise of social media, particularly Twitter, textual data containing users’ opinions on various topics has significantly increased. So sentiment analysis, which involves detecting the polarity of emotions in texts, has also grown. It uses machine learning techniques to accurately interpret and categorize texts, and record public sentiments in different areas. Sentiment analysis improves services and increases user satisfaction. In recent research, deep learning has gained considerable attention in sentiment analysis. Previous sentiment analysis studies have struggled with inadequate performance, focusing on words separately and not sufficiently on contextual information. Our research emphasizes the surrounding context of words and background information. Notably, complex and deep architectures perform better in learning intricate human emotions. Our context-based model elevates previous models utilizing BERT with attention mechanisms alongside Dilated Convolutional Neural Networks. This model results in a broader window for capturing local patterns after bidirectional Recurrent Neural Network layers resulting in a satisfactory sentiment analysis system. The accuracy of this research has improved, demonstrating considerable performance compared to previous studies.
We experimentally demonstrate a silicon photonic colorless and power-efficient thermo-optic switch based on mode-looped phase shifters. The fabricated switch achieves a low P π of 11.1 mW, and extinction ratios over...
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ISBN:
(数字)9798350361957
ISBN:
(纸本)9798350361964
We experimentally demonstrate a silicon photonic colorless and power-efficient thermo-optic switch based on mode-looped phase shifters. The fabricated switch achieves a low P
π
of 11.1 mW, and extinction ratios over 10 dB across a 325 nm wavelength range from 1350 nm to 1675 nm.
The use of millimeter-wave (mmWave) and full-dimensional multiple-input multiple-output (FD-MIMO) antenna systems for 3D wireless communication is being exploited for enhanced network capacity improvement in the ongoi...
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The governing bodies set across different regions have several rules and regulations for the safety of motorcyclists, which they must adhere to. Accidents are undesirable events that result in injuries, or sometimes e...
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ISBN:
(数字)9798350382976
ISBN:
(纸本)9798350382983
The governing bodies set across different regions have several rules and regulations for the safety of motorcyclists, which they must adhere to. Accidents are undesirable events that result in injuries, or sometimes even worse, deaths. These accidents could be averted by wearing helmets while riding. The idea of integrating the output of the image processing algorithms with the ignition system stems from the increased fatality rate of motorcyclists on the road. Therefore, this project aims to reduce such accidents by using a camera tracker as a sensor, where it is further implemented with image processing hardware, such as the Raspberry Pi Camera Module, which runs image processing algorithm to determine if helmet is being worn by the rider or not. Furthermore, Raspberry Pi Camera Module is connected to the ECU using appropriate communication protocols, and the ECU sends outputs that control the ignition system. For the helmet detection, control logics on microcontroller are developed in a way to send signals to the ECU regarding helmet detection status. This project is focused on improving safety and reducing accidents.
We present a novel approach for efficient task scheduling on hierarchical fog nodes, catering to real-time (RT) and non-real-time (NRT) tasks with varying sizes and deadline constraints. Leveraging machine learning (M...
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ISBN:
(数字)9798331509118
ISBN:
(纸本)9798331509125
We present a novel approach for efficient task scheduling on hierarchical fog nodes, catering to real-time (RT) and non-real-time (NRT) tasks with varying sizes and deadline constraints. Leveraging machine learning (ML) techniques, our proposed solution autonomously allocates tasks across fog nodes, dynamically adapting to changing workload patterns and system conditions. Our novel methodology integrates supervised learning algorithms to predict workload patterns and resource availability, enabling intelligent decision-making in task assignment. Our findings contribute to advancing the state-of-the-art in fog computing by offering a scalable and adaptive solution for dynamic task scheduling in hierarchical heterogeneous environments.
Trust and security are critical deployment require-ments for Industrial Internet of Things (IIoT) networks. A recent protocol, called TRUTH, integrates security mechanisms for authentication and privacy alongside a De...
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The research aimed to explore the utilisation of artificial intelligence (AI) in the writing activities of the online student program at one university in Indonesia. A quantitative research design with a survey approa...
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